composer.functional.cutout_batch(input, num_holes=1, length=0.5, uniform_sampling=False)[source]#

See CutOut.

  • input (Image | Tensor) โ€“ Image or batch of images. If a torch.Tensor, must be a single image of shape (C, H, W) or a batch of images of shape (N, C, H, W).

  • num_holes โ€“ Integer number of holes to cut out. Default: 1.

  • length (float, optional) โ€“ Relative side length of the masked region. If specified, length is interpreted as a fraction of H and W, and the resulting box is a square with side length length * min(H, W). Must be in the interval \((0, 1)\). Default: 0.5.

  • uniform_sampling (bool, optional) โ€“ If True, sample the bounding box such that each pixel has an equal probability of being masked. If False, defaults to the sampling used in the original paper implementation. Default: False.


X_cutout โ€“ Batch of images with num_holes square holes with dimension determined by length replaced with zeros.


from composer.algorithms.cutout import cutout_batch
new_input_batch = cutout_batch(X_example, num_holes=1, length=0.25)